Adaptive constitutional networks

Keywords Adaptation Constitutional dynamic chemistry Constitutional networks Dynamic materials Hydrogen bonding Imine formation Supramolecular chemistry... 

Besides the two most well-known cases, the local bifurcations of the saddle-node and Hopf type, biochemical systems may show a variety of transitions between qualitatively different dynamic behavior [13, 17, 293, 294, 297 301]. Transitions between different regimes, induced by variation of kinetic parameters, are usually depicted in a bifurcation diagram. Within the chemical literature, a substantial number of articles seek to identify the possible bifurcation of a chemical system. Two prominent frameworks are Chemical Reaction Network Theory (CRNT), developed mainly by M. Feinberg [79, 80], and Stoichiometric Network Analysis (SNA), developed by B. L. Clarke [81 83]. An analysis of the (local) bifurcations of metabolic networks, as determinants of the dynamic behavior of metabolic states, constitutes the main topic of Section VIII. In addition to the scenarios discussed above, more complicated quasiperiodic or chaotic dynamics is sometimes reported for models of metabolic pathways [302 304]. However, apart from few special cases, the possible relevance of such complicated dynamics is, at best, unclear. Quite on the contrary, at least for central metabolism, we observe a striking absence of complicated dynamic phenomena. To what extent this might be an inherent feature of (bio)chemical systems, or brought about by evolutionary adaption, will be briefly discussed in Section IX. 

Fig. 19 Evolution of a constitutional dynamic system under the pressure of an effector E, leading to adaptation through generation of an enforced distribution (top). Graphical representation of the evolution of the corresponding dynamic network as a weighted square graph (bottom)...

Ulrich S, Lehn JM (2009) Adaptation and optical signal generation in a constitutional dynamic network. Chem Eur J 15 5640-5645... 

The artificial neural network is a simulation and approximation of the biological neural network, and simulates the biological neural networks from the structure, mechanism and function. From the viewpoint of the system, the artificial neural network is an adaptive nonlinear dynamic system constituted by a large number of neurons through extremely rich and perfect connection. From the viewpoint of the system, the artificial neural network is an adaptive nonlinear dynamic system constituted by a large number of neurons through extremely rich and perfect connection. 

Conventional principles and methods concern the synthetic routes for macromo-lecular networks and the realization of the liquid crystalline state by mesogenic monomer tmits. Network chemistry has to consider the reactivity and functionality of the monomer tuiits. In most cases, this excludes ionic polymerization techniques and reduces utihzable methods to radical polymerization and polymer analog reactions for side chain networks, and to polycondensation or polyadditirai reactions for main chain elastomers. The chemistry of the crosslinking process and the chemical constitution of the crosslinker have to be adapted to the polymerization process. Applying photo-chemistry of suitable functional mmiomer units opens an additional, versatile pathway to build up the network structure. 

Fig. 12 Adaptive networks of constitutional dynamic polymers. Two-dimensional network representation of the effector-driven adaptation of the set of dynamers PI, P2, P3, and P4 in response to a chemical effector, the sodium cation Na" (see Fig. 11). The initial, close to statistical distribution of the four dynamers is strongly modified by addition of the cations, leading to an enforced distribution that displays a strong upregulation of P3, which binds Na, and the simultaneous increase of its agonist P4, whereas the antagonists P2 and P3 are strongly downregulated...

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